1. Field of Invention
The present invention relates generally to the field of location-based tracking. More specifically, the present invention is related to relevance assessment for location information received from multiple sources.
2. Discussion of Prior Art
Our world of people and networks of computers is undergoing a paradigm shift: traditional, wired networks are being replaced with their wireless counterparts and, as a result, the ability to stay connected while on the move is becoming a reality, if not a necessity. Concurrent with the mobility trend is another advancement that is equally significant: the fact that those on the move can be tracked. That is, the position of people and other mobile objects can be determined in a geographical or other coordinate space and their location data can be used for subsequent computation.
As a first step in attempting to use location data for something useful, wireless service providers (notably cellular phone operators), content providers, and application developers are busy creating ways of delivering information to their mobile users that is specific to the user's location and therefore presumably more relevant.
This capability, while arguably beneficial and even profitable to users, relies on one important factor that is increasingly difficult to guarantee as technology evolves and users become more sophisticated: that the location of a user can be simply and unambiguously determined. It is not uncommon for a sophisticated user to carry multiple tracking devices, and leave electronic trails wherever financial transactions are executed. The existence of multiple sources of location data increases the possibility of conflicting location information, both temporally (sources are not in sync) and spatially (sources report different locations altogether).
What is needed is a methodology for determining the true location of a mobile object (person, vehicle, computer, etc), given inputs from multiple tracking devices associated with that mobile object. What is needed is a methodology for determining the true location of a mobile object when presented with conflicting location reports.
There is a large body of prior art with regard to the basic elements required for location-based computing: location determination of individual mobile devices, location data formats and protocols for mobile and computer networks, and applications and indexing geographical information systems (GIS). A few such prior art technologies are described below.
Geographical Information Systems
In GIS systems, digital terrain data is combined with other geographical data on roads, pipelines, and buildings for planning purposes (e.g., flood control). When combined with demographic data, it becomes a valuable tool for marketing and business planning. The data is largely static and well-suited for traditional database processing with appropriate spatial extensions, such as multi-dimensional R-tree index. Standards that define spatial data types, operators, and predicates for query processing have also emerged, e.g., structured query language or SQL for multimedia.
Mobile and Computer Networks
Geographical location information has yet to find its true home in computer network protocol stacks. The Domain Name Service (DNS) provides a way to store geographical information about a server, network, or sub-network in a DNS record, but this feature is rarely used. In the mid-to-late 1990's, the computer network equipment industry was active in defining a Spatial Location Protocol (SLOP) for exchanging geographical location over an IP network. This effort migrated to the Mobile Access Interest Group at the World Wide Web Consortium (W3C) in early 2000. More recently, a location-based services forum driven by the mobile phone industry has emerged at the Location Interoperability Forum (LIF). On the World Wide Web, a promising source of location information is to extract (or “mine”) it from Web pages themselves.
Location Tracking of Mobile Phones
The evolution of location determination technology for mobile phones has accelerated in the last few years, largely fueled by the expanding use of mobile phones, but also the United States Federal Communications Commission's (FCC) mandate that the originating location of all emergency 911 calls from mobile phones be determined with a defined accuracy. Today wireless service operators have a choice of half of a dozen technologies, each with different power consumption, handset compatibility, time-to-first-fix, and in-building coverage characteristics.
Traditional methods where a mobile device would autonomously determine its location (e.g., using the Global Positioning Satellite (GPS) system) are falling out of flavor and are being replaced by network-based or hybrid solutions. For example, in the hybrid assisted GPS (A-GPS) system, part of the location computation is done on the network. The network-based Angle-of-Arrival (AOA) approach requires special antenna arrays in base stations but works with any radio transmitter. The Cell-of-Origin (COO) approach combines information about the cell where the mobile phone is located and the timing advance used to synchronize the mobile phone with the base station. The result is an arc around the base station with a defined width—the mobile phone is located somewhere along this arc.
Newer location determination methods include Time-Difference-of-Arrival (TDOA) and Enhanced Observed Time Difference (EOTD). In TDOA, three or more time-synchronized base stations receive a radio transmission from a mobile phone and triangulate its location based on the arrival time of the transmission from a mobile phone and triangulate its location based on the arrival time of the transmission at each base station. In EOTD, the location is determined by comparing the arrival time of a signal from three or more base stations at the mobile phone and at a fixed reference point known as the location measurement unit. The use of an external reference point obviates the need for base stations to remain time-synchronized, but in contrast, it requires modification of the handset and additional network equipment.
It should be noted that all methods except AOA rely in computing the time difference between radio transmission and reception. Triangulation method based on signal attenuation is also possible, but it is very unreliable because radio signals attenuate for reasons other than distance traveled: walls, foliage, vehicles, and other obstacles. It also requires knowing the radio transmission power, which may be difficult to determine.
Some mobile objects are embedded with a native tracking capability, while others are tracked by other mobile objects that they are associated with. For instance, humans are not natively tracked, but the devices they carry (mobile phones and wireless PDAs) and the transportation they use can be tracked. It is important to note that the association between a source mobile object (which can be tracked) and a target mobile object (which “receives” or relies on the location data from the source) is parameterized by an association confidence that indicates the probability that the target object is at the location indicated by the source object. For instance, a person may be associated with a mobile phone 100% of the time, while his/her association with a vehicle that is time-shared with family members may be only 60%. The corresponding association confidence values are 1.0 and 0.6 respectively.
Association confidence requires a distinction between the case where the source mobile moves (moving association confidence) and the case where it is stationary (stationary association confidence). A vehicle, for instance, is a good indicator of the location of a person only when the vehicle is moving. When the vehicle is stationary (parked), it is unlikely that the person who normally drives it is actually in the vehicle. The distinction is even more pronounced in a work environment. A wireless laptop, for instance, may be highly personal to an employee and a good indicator of that employee's location within the building, but only if that laptop is moving. When the laptop is stationary inside the employee's office, it is not clear whether the user is also in the office or not. On the other hand, keyboard/mouse activity detection can be used in making an informed decision.
Another dimension of location determination is offered by the stationary objects that detect the presence of mobile objects. A person who purchases items at different point-of-sale (POS) terminals using his/her credit card leaves an electronic trail that can be tracked. Similarly, an employee carrying a radio or infrared beacon may be detected by receivers installed throughout an office building.
While technically quite different from tracking the movement of mobile objects directly, the electronic trail method can be conceptually viewed as being analogous to a virtual tracking device associated with the mobile objects. When the presence of the mobile object is detected at a stationary object, it is as if the virtual tracking device generated a location report at that location.
What makes it distinct from direct observation of a mobile object is that the mobile object is unaware of the detection event, and hence, the location tracking event. In direct observation, a self-positioning device is aware of its own location.
In order to develop a generic method for determining the position of any mobile object, it is useful to classify different mobile objects according to how they move and how they can be tracked. Six such categories of mobile objects are discussed below.
People
While most people do not wish to be tracked at all, the reality is that many of us can be tracked using several technologies. While a person is not directly trackable, the many trackable mobile objects he/she is associated with reveal the person's location. For example, various modes of transportation (airlines, trains, buses, and private vehicles), or the electronic trail left at shops and banks where electronic transactions are performed (using our credit cards or identity cards) can be tracked. Furthermore, small mobile devices such as cell phones and wireless PDAs can also be tracked. Additionally, in a business environment it is particularly common to associate location information with specific calendar events (which are maintained via electronic calendars used for time management).
Large Physical Assets
Large physical assets such as vessels, aircraft and large vehicles (especially in the construction and transportation industry) are usually owned by businesses, not individual people. Therefore, they tend to move along predefined business routes and a deviation from such routes could indicate trouble. Large assets are frequently tracked for navigational aid but also for preventing asset loss or assisting recovery after a loss. They are typically associated with one directly trackable mobile object (e.g., embedded GPS receiver and beacon) plus several electronic trails (e.g., harbor arrival and departure, air traffic control, and highway and bridge tolls).
Small Physical Assets
Smaller physical assets include computers, PDAs, private vehicles, and pets. They are highly personal and therefore tend to move wherever their owners moves. While computers and PDAs may be tracked by their network activity (electronic trail), in realistic scenarios these assets are not directly trackable. Instead, as with people, their position is determined by their association with other mobile objects.
Wireless Assets
Mobile phones, pagers, wireless PDAs and computers, and GPS devices fall under this category called wireless assets. They are natively tracked using one of the location determination technologies discussed earlier. Wireless assets may be able to determine their own position (self-positioning devices). They tend to be associated with exactly one additional mobile object and move in unison with that object. The reverse, however, is not true; a mobile object may be associated with several wireless assets.
Identity Assets
When credit cards are used at a point-of-sale (POS) terminal or when identity cards are used to gain access to a building or an account, they leave an electronic trail. Today's cards rarely have computational power embedded in them (except smart cards which have an embedded microprocessor as well as memory capacity) and therefore are unable to determine their own position. Credit cards, identity cards, and other identity assets (e.g., keys) are highly personal and tend to move wherever the owner moves.
Transportation Items
Transportation items such as physical packages and data packets are typically moved independently of their owner. However, items may be associated with a person or business at each end of the route of transportation. While points near the start (or end) or the transportation route are approximations of where the sender (or recipient) resides, the intermediate points along the route bare less significance on the location.
Physical packages are typically tracked by the delivery network that scans a barcode attacked to the package at each point along the delivery route. The same is true for data packets. While the sheer number of data packets may prevent tracking them all at once, network routers can be configured to track network activity of a specific host. If the physical location of routers is known, the electronic trail of the data packet can be established.
If a mobile phone reports the location of a tracked entity as being in San Jose, Calif., and five minutes later a GPS-equipped car belonging to the same entity is detected in San Francisco, some 50 miles away, there is a conflict in the reported locations and prior art systems fail to resolve such conflicting reports. More formally, given two location reports with distinct accuracy, temporal, and associative confidence characteristics, prior art systems fail to provide for a methodology to determine which of the conflicting reports is more relevant.
Thus, prior art systems fail to provide for an efficient system or method to measure the reliability of such conflicting location reports. Furthermore, such prior art systems fail to use measures of reliability to pick one report as being more accurate over the other.
The following references describe location tracking systems in general, but they fail to provide a system that can simultaneously receive multiple location reports from one or more location tracking devices (associated with an entity being tracked) and resolve any conflicts in the location reports.
The non-patent literature entitled, “Active Tracking: Locating Mobile Users In Personal Communication Service Networks” provides for a general description of the problem of tracking mobile users in a personal communication service (PCS) network. The non-patent literature, however, fails to mention receiving reports from more than one device associated with a trackable entity.
The non-patent literature entitled, “Paging Area Optimization Based On Interval Estimation In Wireless Personal Communication Networks” provides for a way to reduce the paging signaling cost by minimizing the size of the paging area constrained to certain confidence measure (probability of locating the user), based on a finite number of available location observations of the mobile user.
The U.S. Patent to Buford et al. (U.S. Pat. No. 5,945,948) provides for a method and apparatus for location finding in a communication system. The method includes the steps of receiving a signal from the subscriber unit at a first base station, determining a first receive time of the signal based on a sequence of spreading symbols at the first base station, determining a first angle of arrival of the signal at the first base station, and determining the location of the subscriber unit from the first receive time, the first angle of arrival, and further predetermined information about the first base station. The signal is formed via modulation by the sequence of spreading symbols.
The U.S. Patent to Hummelsheim (U.S. Pat. No. 6,192,312 B1) provides for a position determining system that helps determine the position of an object, such as a vehicle, relative to a road network represented by a geographic database. The method described attempts to improve the precision of existing reports by averaging them or otherwise calculating probable locations.
Whatever the precise merits, features and advantages of the above cited prior art references, none of them achieve or fulfills the purposes of the present invention.